methodology
Methodology
The quantitative framework behind on-chain intelligence
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The quantitative framework behind on-chain intelligence
We combine deep learning with blockchain data analysis to identify high-probability trading zones. Our methodology prioritizes statistical rigor over heuristics, transparency over black boxes.
Our models analyze behavioral patterns across millions of wallets, identifying consistent alpha generators through neural network classification rather than simple rule-based filtering.
Every signal we generate undergoes statistical validation. We don't publish zones that lack significance—even if they look compelling on a chart.
We publish our methodology, track our performance publicly, and provide clear scoring criteria. You can verify our claims against actual outcomes.
Our quantitative framework draws from established disciplines:
Risk-adjusted returns, portfolio theory, factor modeling
Neural networks for behavioral pattern recognition
Wallet relationship mapping and cluster detection
Temporal pattern detection and regime identification
Our methodology evolves with the market. We monitor zone performance daily, A/B test algorithm changes against control groups, and incorporate new research findings as they emerge.
We publish quarterly methodology updates detailing changes, their rationale, and measured impact on zone accuracy.
We're committed to increasing transparency over time while protecting the research that makes our signals valuable.
| Initiative | Status | Description |
|---|---|---|
| Third-Party Audit | Planned | Independent verification by blockchain analytics firm |
| Performance Verification | Planned | Quarterly reports with on-chain proof of timestamps |
| Open-Source Components | In Progress | Non-proprietary infrastructure components |
Why not fully open source? We balance transparency with protecting research. Fully open-sourcing scoring weights would enable front-running and degrade zone quality. Our approach: open process, protected parameters.